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algorithm python » algorithms within (Expand Search), algorithm both (Expand Search)
within function » fibrin function (Expand Search), protein function (Expand Search), catenin function (Expand Search)
python function » protein function (Expand Search)
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41
Fitness comparison on test function.
Published 2025“…The study employed traditional benchmark functions and conducted evaluations versus baselines Standard GEP, NMO-SARA, and MS-GEP-A to assess fitness outcomes, R² values, population diversification, and the avoidance of local optima. …”
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A comparison of whether to add a penalty.
Published 2025“…An innovative algorithm, the improved hybrid gray wolf and whale algorithm fused with a penalty function for construction machinery optimization (IHWGWO), is introduced, incorporating a penalty function to handle constraints effectively. …”
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NRPStransformer, an Accurate Adenylation Domain Specificity Prediction Algorithm for Genome Mining of Nonribosomal Peptides
Published 2025“…Leveraging the sequences within the flavodoxin-like subdomain, we developed a substrate specificity prediction algorithm using a protein language model, achieving 92% overall prediction accuracy for 43 frequently observed amino acids, significantly improving the prediction reliability. …”
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Multimodal reference functions.
Published 2025“…Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …”
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R-squared comparison of test function.
Published 2025“…The study employed traditional benchmark functions and conducted evaluations versus baselines Standard GEP, NMO-SARA, and MS-GEP-A to assess fitness outcomes, R² values, population diversification, and the avoidance of local optima. …”
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The convergence curves of the test functions.
Published 2025“…Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …”
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Single-peaked reference functions.
Published 2025“…Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …”
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